title: Solving the paradox of stasis: squashed stabilizing selection and the limits of detection.
abstract: Despite the potential for rapid evolution, stasis is commonly observed over geological timescales-the so-called "paradox of stasis." This paradox would be resolved if stabilizing selection were common, but stabilizing selection is infrequently detected in natural populations. We hypothesize a simple solution to this apparent disconnect: stabilizing selection is hard to detect empirically once populations have adapted to a fitness peak. To test this hypothesis, we developed an individual-based model of a population evolving under an invariant stabilizing fitness function. Stabilizing selection on the population was infrequently detected in an "empirical" sampling protocol, because (1) trait variation was low relative to the fitness peak breadth; (2) nonselective deaths masked selection; (3) populations wandered around the fitness peak; and (4) sample sizes were typically too small. Moreover, the addition of negative frequency-dependent selection further hindered detection by flattening or even dimpling the fitness peak, a phenomenon we term "squashed stabilizing selection." Our model demonstrates that stabilizing selection provides a plausible resolution to the paradox of stasis despite its infrequent detection in nature. The key reason is that selection "erases its traces": once populations have adapted to a fitness peak, they are no longer expected to exhibit detectable stabilizing selection.
authors.affiliation: Department of Biology and Redpath Museum, McGill University, 859 Sherbrooke Street West, Montreal, Quebec, Canada, H3A 0C4. benjamin.haller@mail.mcgill.ca.
authors.forename: Benjamin C
authors.initials: BC
authors.lastname: Haller
authors.forename: Andrew P
authors.initials: AP
authors.lastname: Hendry
mesh_terms: D019143:Evolution, Molecular; D056084:Genetic Fitness; D014644:Genetic Variation; D008957:Models, Genetic; D012641:Selection, Genetic
keywords: Competition; directional selection; disruptive selection; fitness landscape; frequency-dependent selection; selection gradient